Performance Comparison of Lossless Compression Strategies for Dynamic Vision Sensor Data

Abstract

Dynamic Vision Sensors (DVS) are emerging neuromorphic visual capturing devices, with great advantages in terms of low power consumption, wide dynamic range, and high temporal resolution in diverse applications. The capturing method results in lower data rates than conventional video. Still, such data can be further compressed. This is an emerging research area and a performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed. According to the detailed experimental analysis, LZMA achieves the best compression ratio among all the considered strategies. On the other hand, Brotli achieves the best trade-off between speed (compression and decompression) and compression ratio.